Characterizing point checking region setting apparatus and method, and image stabilizing apparatus including the same

ABSTRACT

Provided is an image stabilizing apparatus and method for correcting an image that is shaken due to a movement of a camera. The image stabilizing apparatus includes a characterizing point checking region setting unit including: a sample frame extract unit which extracts a plurality of image frames obtained for a certain period of time in image data obtained by photographing an object; and a frame analyzing unit which detects a plurality of characterizing points in the extracted plurality of image frames, and sets a characterizing point checking region which is used to check characterizing points in a currently input image frame.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This application claims priority from Korean Patent Application No.10-2012-0003450, filed on Jan. 11, 2012 in the Korean IntellectualProperty Office, the disclosure of which is incorporated herein in itsentirety by reference.

BACKGROUND

1. Field

Apparatuses and methods consistent with exemplary embodiments relate toimage stabilization, and more particularly, to a characterizing pointchecking region setting apparatus and method, and an image stabilizingapparatus including the characterizing point checking region settingapparatus.

2. Description of the Related Art

In order to exactly detect a certain object, in particular, a movingobject, by using a camera, each image has to be stabilized. However, itmay be difficult to detect a certain object because captured images areshaken due to various external causes. For example, when a certainobject is photographed in a state where a camera is exposed to anoutside environment, the camera may slightly move due to, for example,wind or external shock. In addition, when the camera is mounted on amovable apparatus, the camera may be shaken according to movement of themovable apparatus. The shaking of images becomes severe as more externalshock is applied to the camera, and eventually the object may not bedetected exactly. An image stabilization technology is used to detect anobject exactly by stabilizing the shaken images.

A Korean patent (KR 2008-0083525; Method for stabilizing digital imagewhich can correct the horizontal shear distortion and vertical scaledistortion) discloses a related art image stabilization method.According to the related art image stabilization method, a current frameis corrected by using characterizing points extracted from the currentframe and characterizing points extracted from a previous frame.According to this image stabilization method, however, if a shakingdegree of the image increases, image correction may not be stablyperformed.

SUMMARY

One or more exemplary embodiments provide an apparatus and method ofsetting an optimal characterizing point checking region, and an imagestabilizing apparatus for correcting and stabilizing shaking images byusing the characterizing point checking region.

According to an aspect of an exemplary embodiment, there is provided acharacterizing point checking region setting unit including: a sampleframe extract unit which extracts a plurality of image frames, obtainedfor a certain period of time, from image data obtained by photographingan object; and a frame analyzing unit which detects a plurality ofcharacterizing points in the extracted plurality of image frames, andsets a characterizing point checking region which is used to checkcharacterizing points in a currently input image frame.

The frame analyzing unit may include: a characterizing point detectorwhich receives the plurality of image frames from the sample frameextract unit, and detects the plurality of characterizing points in theplurality of image frames; a characterizing point classification unitwhich classifies the plurality of characterizing points into a pluralityof clusters for each of the image frames; a center point detector whichdetects a centroid point of the characterizing points in the pluralityof image frames; and a check region determination unit which sets thecharacterizing point checking region including a major cluster among theplurality of clusters based on the centroid point.

According to an aspect of another exemplary embodiment, there isprovided a characterizing point checking region setting methodincluding: receiving a plurality of image frames captured for a certainperiod of time; and detecting a plurality of characterizing points inthe plurality of image frames, and setting a characterizing pointchecking region for checking the detected characterizing points, in acurrently input image frame.

The setting the characterizing point checking region may include:detecting a plurality of characterizing points in each of the pluralityof image frames; classifying the plurality of characterizing points intoa plurality of clusters for each of the image frames; detecting arepresentative centroid point representing the plurality of imageframes; and setting the characterizing point checking region including amajor cluster among the plurality of clusters, based on therepresentative centroid point.

According to an aspect still another exemplary embodiment, there isprovided an image stabilizing apparatus including: the abovecharacterizing point checking region setting unit; and an imageadjusting unit which sets the characterizing point checking region in animage frame that is currently input, compares the currently input imageframe with a reference image that is preset, and adjusts the currentlyinput image frame as much as a shaking amount when it is determined thatthe current image frame is shaken.

The image stabilizing apparatus may further include: a reference imagesetting unit which extracts an image frame that is the least shakenamong the plurality of image frames taken for a certain period of time,and sets the extracted image frame as the reference image.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other aspects will become more apparent by describing indetail exemplary embodiments with reference to the attached drawings, inwhich:

FIG. 1 is a block diagram of an image stabilizing apparatus according toan exemplary embodiment;

FIG. 2 is a detailed block diagram of a reference image setting unitshown in FIG. 1, according to an exemplary embodiment;

FIGS. 3A, 3B and 3C show examples of shaken image frames and stabilizedimage frames;

FIG. 4 is a flowchart illustrating a method of setting a reference imageperformed by the reference image setting unit shown in FIG. 2, accordingto an exemplary embodiment;

FIG. 5 is a flowchart illustrating an operation of the method shown inFIG. 4 in detail, according to an exemplary embodiment;

FIG. 6 is a detailed block diagram of a characterizing point checkingregion setting unit shown in FIG. 1, according to an exemplaryembodiment;

FIG. 7 is a diagram showing examples of detected centroid pointsaccording to an exemplary embodiment;

FIGS. 8A and 8B are diagrams illustrating a method of setting acharacterizing point checking region, according to an exemplaryembodiment;

FIGS. 9A and 9B are diagrams of set optimal characterizing pointchecking regions according to an exemplary embodiment;

FIG. 10 is a flowchart illustrating a method of setting a characterizingpoint checking region performed by the characterizing point checkingregion setting unit shown in FIG. 6, according to an exemplaryembodiment;

FIG. 11 is a flowchart illustrating a second operation of the methodshown in FIG. 10 in detail, according to an exemplary embodiment;

FIG. 12 is a detailed block diagram of an image adjusting apparatusshown in FIG. 1, according to an exemplary embodiment;

FIG. 13 is an image showing an example of an optical flow according toan exemplary embodiment;

FIG. 14 is a diagram showing representative directions of the opticalflow according to an exemplary embodiment;

FIG. 15 is an image showing a state where an image is adjusted accordingto an exemplary embodiment;

FIGS. 16A and 16B are graphs showing shaken degrees of image frames,according to an exemplary embodiment; and

FIG. 17 is a flowchart illustrating a method of adjusting an imageperformed by the image adjusting apparatus of FIG. 12, according to anexemplary embodiment.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

Hereinafter, exemplary embodiments will be described in detail withreference to accompanying drawings. Like reference numerals denote likeelements.

FIG. 1 is a block diagram of an image stabilizing apparatus 100according to an exemplary embodiment. The image stabilizing apparatus100 receives image data P1 that is obtained by a camera (not shown)photographing an object, and stabilizes images included in the imagedata P1. When the object is continuously photographed by the camera in astate of being fixed, obtained images are stabilized. However, if theobject is photographed in a state where the camera is shaken, obtainedimages are also shaken, and accordingly the photographed object may notexactly be distinguished from other objects or an environment. When theimages are shaken as described above, the image stabilizing apparatus100 stabilizes the image by moving the shaking object to an originalposition in the image.

Referring to FIG. 1, the image stabilizing apparatus 100 includes areference image setting unit 111, a characterizing point checking regionsetting unit 121, and an image adjusting unit 131.

The reference image setting unit 111 extracts an image frame that isshaken least among a plurality of image frames included in the imagedata P1 obtained by photographing the object, and then sets theextracted image frame as a reference image. The reference image settingunit 111 outputs a signal P2 indicating the reference image to the imageadjusting unit 131. The reference image setting unit 111 is described inmore detail below with reference to FIGS. 2 through 5.

The characterizing point checking region setting unit 121 receives theimage data P1 input from outside, and sets a characterizing pointchecking region. The characterizing point checking region setting unit121 generates a signal P3 indicating the characterizing point checkingregion, and outputs the signal P3 to the image adjusting unit 131. Thecharacterizing point checking region setting unit 121 will be describedin detail with reference to FIGS. 6 through 11.

The image adjusting unit 131 receives the signals P2 and P3. The imageadjusting unit 131 sets the characterizing point checking region in theimage included in the image data P1 that is currently input, andcompares the currently input image with the reference image so as toadjust and stabilize the currently input image according to a shakendegree of the image when the currently input image is shaken. The imageadjusting unit 131 is described in more detail below with reference toFIGS. 12 through 17.

FIG. 2 is a detailed block diagram of the reference image setting unit111 shown in FIG. 1. Referring to FIG. 2, the reference image settingunit 111 includes a sample frame extract unit 211 and a reference frameextract unit 221.

The sample frame extract unit 211 receives the image data P1 fromoutside. The image data P1 is obtained by continuously photographing anobject with the camera. The image data P1 includes a plurality of imageframes. For example, the image data P1 includes a plurality of imageframes as shown in FIGS. 3A, 3B and 3C, each including a buildinglocated on a right side of the image frame. The image frames in FIG. 3Aand 3C show states where images are shaken vertically relative to theimage frame in FIG. 3B. The sample frame extract unit 211 extracts aplurality of image frames taken for a certain time period from the imagedata P1. The image data P1 may include hundreds to tens of thousands ofimage frames per second according to performance of the camera.Therefore, the certain period of time may be set as 1 second or shorterif a shutter speed of the camera is fast, and may be set to be longerthan 1 second if the shutter speed of the camera is slow. However, thepresent embodiment is not limited to this example.

The reference frame extract unit 221 receives the plurality of imageframes extracted by the sample frame extract unit 211, and compares thereceived image frames with each other to extract the most stabilizedimage frame and sets the most stabilized image frame as a referenceimage. The most stabilized image frame is an image frame of which ashaking degree is the least.

The reference frame extract unit 221 includes a center point detector231, a characterizing point detector 232, a frame average calculator233, a frame comparison value calculator 234, and a reference frameselector 235.

The center point detector 231 receives the plurality of image framesfrom the sample frame extract unit 211, and detects center points of theplurality of image frames. That is, the center point detector 231detects one center point from each of the plurality of image frames. Thecenter point is located at a center of the image frame and may berepresented as coordinates.

The characterizing point detector 232 receives the plurality of imageframes from the sample frame extract unit 211 and detects a plurality ofcharacterizing points in the plurality of image frames. That is, thecharacterizing point detector 232 detects the plurality ofcharacterizing points in each of the plurality of image frames. Theplurality of characterizing points may be represented as coordinates.The image frame includes various characterizing elements, some of whichmay be detected as the characterizing points according to needs of auser. In order to detect the characterizing points of the image frame, aHarris' corner detection method, a scale invariant feature transform(SIFT) algorithm, or a speeded-up robust feature (SURF) algorithm may beused.

The frame average calculator 233 receives the plurality of center pointsdetected by the center point detector 231 and the plurality ofcharacterizing points detected by the characterizing point detector 232,and calculates a plurality of frame averages. The plurality of frameaverages may be obtained by averaging distances between the centerpoints and the plurality of characterizing points in corresponding imageframes. When the number of image frames is N (N is an integer greaterthan zero), N frame averages may be calculated.

The frame comparison value calculator 234 receives the plurality offrame averages from the frame average calculator 233 and calculates aplurality of frame comparison values. The plurality of frame comparisonvalues may be obtained by summing up absolute values, which are obtainedby subtracting the frame averages of other image frames from the frameaverage of the corresponding image frame. If the number of the frameaverages is N, the number of the frame comparison values is also N. Theframe comparison value Pk (k is an integer) of each of the plurality ofimage frames may be calculated by the following equation 1.Pk=abs{Rk−R0}+abs{Rk−R1}+ . . . +abs{Rk−Rn}  (1),

where abs denotes an absolute value.

For example, if the number of extracted image frames for the certainperiod of time is 5, five frame averages R0 to R4 are calculated, andfive frame comparison values P0 to P4 may be obtained as the followingequation 2.P0=abs{R0−R1}+abs{R0−R2}+abs{R0−R3}+abs{R0−R4}P1=abs{R1−R0}+abs{R1−R2}+abs{R1−R3}+abs{R1−R4}P2=abs{R2−R0}+abs{R2−R1}+abs{R2−R3}+abs{R2−R4}P3=abs{R3−R0}+abs{R3−R1}+abs{R3−R2}+abs{R3−R4}P4=abs{R4−R0}+abs{R4−R1}+abs{R4−R2}+abs{R4−R3}  (2)

The reference frame selector 235 receives the plurality of framecomparison values and selects an image frame having the smallest framecomparison value among the plurality of frame comparison values. Theimage frame having the smallest value is set as the reference image. Thesmallest frame comparison value represents that the image is leastshaken.

As described above, the reference image setting unit 111 extracts theplurality of image frames for the certain period of time from the imagedata P1 input from outside and detects an image frame having the leastdegree of shaking among the extracted image frames and sets this imageframe as the reference image.

FIG. 4 is a flowchart illustrating a method of setting the referenceframe by the reference image setting unit 111 of FIG. 2. Referring toFIG. 2, the method of setting the reference image includes operationS411 and operation S421.

In operation S411, the reference image setting unit 111 extracts theplurality of image frames taken for a certain period of time among theplurality of image frames included in the image data P1 input fromoutside.

In operation S421, the reference image setting unit 111 compares theplurality of extracted image frames with each other to detect and setthe image frame that is the most stabilized as the reference image. Themost stabilized image frame denotes an image frame, of which a shakingdegree is the least among the image frames.

FIG. 5 is a flowchart illustrating the operation S421 of FIG. 4 in moredetail. Referring to FIG. 5, the operation S421 of FIG. 4 includes foursub-operations S511 through S541.

In operation S511, the reference image setting unit 111 extracts thecenter point and the plurality of characterizing points from each of theplurality of extracted image frames.

In operation S521, the reference image setting unit 111 calculates anaverage of distances between the center points and the plurality ofcharacterizing points in each of the image frames, that is, a frameaverage.

In operation S531, the reference image setting unit 111 calculates a sumof absolute values that are obtained by subtracting other frame averagesfrom the frame average of each image frame, that is, a frame comparisonvalue. That is, the reference image setting unit 111 calculates theplurality of frame comparison values by using equation 1 above.

In operation S541, the reference image setting unit 111 detects an imageframe having the smallest frame comparison value among the plurality offrame comparison values and sets the detected image frame as thereference image.

Therefore, the reference image setting unit 111 detects the image frameof which a shaking degree is the least among the plurality of imageframes included in the image data P1 and then sets the detected imageframe as the reference image.

FIG. 6 is a detailed block diagram of the characterizing point checkingregion setting unit 121 shown in FIG. 1. Referring to FIG. 6, thecharacterizing point checking region setting unit 121 includes a sampleframe extract unit 611 and a frame analyzing unit 621.

The sample frame extract unit 611 receives the image data P1 fromoutside. The image data P1 includes a plurality of image frames that areobtained by photographing an object continuously. The sample frameextract unit 611 extracts a plurality of image frames taken for acertain time period among the plurality of image frames included in theimage data P1. The image data P1 obtained by photographing the objectwith the camera may include hundreds to tens of thousands of imageframes per second according to performance of the camera. Therefore, thecertain period of time may be set as 1 second or shorter if the shutterspeed of the camera is fast, and may be set to be longer than 1 secondif a shutter speed of the camera is slow. However, the presentembodiment is not limited to this example.

The frame analyzing unit 621 receives the plurality of image frames thatare extracted for the certain period of time from the sample frameextract unit 611. The frame analyzing unit 621 detects a plurality ofcharacterizing points in the plurality of image frames, and sets anoptimal characterizing point checking region by using the plurality ofcharacterizing points. The frame analyzing unit 621 outputs a signal P3representing the characterizing point checking region.

The frame analyzing unit 621 includes a characterizing point detector631, a characterizing point classification unit 632, a center pointdetector 633, a checking region setting unit 634, and a checking regionadjusting unit 635.

The characterizing point detector 631 receives the plurality of imageframes extracted for the certain period of time from the sample frameextract unit 611 and detects a plurality of characterizing points (723,733 of FIG. 7) in each of the plurality of image frames. That is, thecharacterizing point detector 631 detects a plurality of characterizingpoints (723, 733 of FIG. 7) in each of the plurality of image frames.Each of the plurality of characterizing points (723, 733 of FIG. 7) maybe represented as coordinates. Each of the image frames includes variouscharacterizing elements, some of which may be detected as thecharacterizing points (723, 733 of FIG. 7) according to setting by auser. In order to detect the characterizing points (723, 733 of FIG. 7)of the image frame, a Harris' corner detection method, a SIFT algorithm,or an SURF algorithm may be used.

The characterizing point classification unit 632 classifies theplurality of characterizing points (723, 733 of FIG. 7) detected by thecharacterizing point detector 631 as a plurality of clusters (721 and731 of FIG. 7), for example, a major cluster (721 of FIG. 7) and a minorcluster (731 of FIG. 7), for each of the image frames (711 of FIG. 7).The major cluster (721 of FIG. 7) includes 50% or more of thecharacterizing points, and the minor cluster (731 of FIG. 7) includesless than 50% of the characterizing points. As described above, sincethe major cluster (721 of FIG. 7) includes more characterizing pointsthan the minor cluster (731 of FIG. 7), the major cluster 721 may bewider than the minor cluster 731 as shown in FIG. 7. In order toclassify the characterizing points (723, 733 of FIG. 7) as a pluralityof clusters (721 and 731 of FIG. 7), a k-mean clustering method and asupport vector machine (SVM) method may be used as an example.

The center point detector 633 detects a centroid point (741 of FIG. 7)of the characterizing points in the plurality of image frames. To dothis, the center point detector 633 detects a centroid point (723 ofFIG. 7) of the major cluster 721 and a centroid point (733 of FIG. 7) ofthe minor cluster 731 that are classified by the characterizing pointclassification unit 632 in each of the image frames (711 of FIG. 7). Thecenter point detector 633 calculates an average between the centroidpoint (723 of FIG. 7) of the major cluster 721 and the centroid point(733 of FIG. 7) of the minor cluster 731 to detect the centroid point(741 of FIG. 7) in each of the image frames. The centroid point (741 ofFIG. 7) in each of the image frames is generally adjacent to the majorcluster (721 of FIG. 7) as shown in FIG. 7. The center point detector633 calculates an average of the centroid points (741 of FIG. 7) of theplurality of image frames, and detects a representative centroid point(751 of FIGS. 8A and 8B) of the plurality of image frames, as shown inFIGS. 8A and 8B. The average of the centroid points (741 of FIG. 7) inthe plurality of image frames may be calculated by summing the centroidpoints (741 of FIG. 7) of the plurality of image frames, and dividingthe sum by the number of image frames. The centroid points describedabove may be represented as coordinates.

The checking region setting unit 634 sets a characterizing pointchecking region (811 of FIG. 8A or 821 of FIG. 8B) including all of themajor clusters (721 of FIG. 7) of the plurality of image frames based onthe representative centroid point (751 of FIGS. 8A and 8B) detected bythe center point detector 633, as shown in FIGS. 8A and 8B. Thecharacterizing point checking region 811 or 821 may be formed in variousshapes, for example, may be formed as a region 811 denoted by a circleas shown in FIG. 8A or may be formed as a region 821 denoted by a squareas shown in FIG. 8B.

The checking region adjusting unit 635 determines whether thecharacterizing point checking region 811 or 821 includes a standardlevel or greater of the characterizing points (921 of FIG. 9B) of theimage frames extracted for the certain period of time. The standardlevel may be set as 80% of the characterizing points 921. The checkingregion adjusting unit 635 expands the characterizing point checkingregion 811 or 821 so as to include the standard level of characterizingpoints, if the characterizing points included in the characterizingpoint checking region 811 or 821 are less than the standard level. FIG.9B shows a state where the adjustment is finished and an optimalcharacterizing point checking region 911 is set, and FIG. 9A shows oneof the plurality of image frames.

As described above, since the characterizing point checking regionsetting unit 121 sets the optimal characterizing point checking region911, a time taken to test the characterizing points of the imagestabilizing apparatus 100 may be greatly reduced.

FIG. 10 is a flowchart illustrating a method of setting thecharacterizing point checking region performed by the characterizingpoint checking region setting unit 121 shown in FIG. 6, according to anexemplary embodiment. Referring to FIG. 10, the method includesoperation S1011 and operation S1021.

In operation S1011, the characterizing point checking region settingunit (121 of FIG. 6) extracts a plurality of image frames taken for acertain period of time among the plurality of image frames included inthe image data (P1 of FIG. 6) input from outside.

In operation S1021, the characterizing point checking region settingunit 121 detects a plurality of characterizing points (921 of FIG. 9B)in the plurality of extracted image frames and sets the optimalcharacterizing point checking region (911 of FIG. 9B) by using theplurality of characterizing points 921.

FIG. 11 is a flowchart illustrating the operation S1021 shown in FIG. 10in more detail. Referring to FIG. 11, the operation S1021 shown in FIG.10 includes sub-operations S1111 through 1151.

In operation S1111, the characterizing point setting unit 121 extracts aplurality of characterizing points (723, 733 of FIG. 7) from each of theplurality of extracted image frames.

In operation S1121, the characterizing point checking region settingunit 121 classifies the plurality of detected polarizing points (723,733 of FIG. 7) as a plurality of clusters (721 and 731 of FIG. 7), forexample, the major cluster 721 and the minor cluster 731, for each ofthe image frames. The major cluster 721 is set to include 50% or greaterof the characterizing points, and the minor cluster 731 is set toinclude less than 50% of the characterizing points.

In operation S1131, the characterizing point checking region settingunit 121 detects the representative centroid point (751 of FIGS. 8A and8B) of the plurality of image frames. That is, the characterizing pointchecking region setting unit 121 detects the centroid points (723 and733 of FIG. 7) from each of the plurality of clusters 721 and 731, andcalculates the average of the centroid points 723 and 733 of theplurality of clusters 721 and 731 for each of the image frames to detectthe centroid point 741 of each of the image frames. In addition, thecentroid points 741 of the plurality of image frames are summed, and thesum is divided by the number of image frames to detect therepresentative centroid point 751 of the plurality of image frames.

In operation S1141, the characterizing point checking region settingunit 121 sets the characterizing point checking region (811 of FIG. 8Aor 821 of FIG. 8B) that includes all of the major clusters 721 based onthe representative centroid point 751.

In operation S1151, the characterizing point checking region settingunit 121 determines whether the characterizing point checking region 811or 821 includes the standard level of characterizing points 921 of theimage frames extracted for the certain period of time or greater. Whenan amount of the characterizing points included in the characterizingpoint checking region 811 or 821 is less than the standard level, thecharacterizing point checking region setting unit 121 expands thecharacterizing point checking region 811 or 821 to include the standardlevel of characterizing points. The standard level may be set as 80% ofthe characterizing points 921. Therefore, the optimal characterizingpoint checking region 911 may be set.

As described above, the characterizing point checking region settingunit 121 sets the optimal characterizing point checking region 911 byusing the plurality of image frames included in the image data P1 inputfrom outside, and thus, a time that is taken to check the characterizingpoints of the image frames is greatly reduced.

FIG. 12 is a detailed block diagram of the image adjusting unit 131shown in FIG. 1. Referring to FIG. 12, the image adjusting unit 131includes an image analyzing unit 1201 and an image moving unit 1241.

The image analyzing unit 1201 compares a current image frame included inthe image data P1 input from outside with the predetermined referenceimage included in the reference image signal P2 and extracts arepresentative direction and a representative magnitude of the shakingif the current image frame is shaken.

The image analyzing unit 1201 includes an optical flow calculator 1211,a representative direction extractor 1221, and a representativemagnitude extractor 1231.

The optical flow calculator 1211 compares the current image frame withthe reference image to calculate an optical flow (1321 of FIG. 13) inthe characterizing point checking region 911. As shown in FIG. 13, theoptical flow 1321 has a direction and a magnitude. A method ofcalculating the optical flow 1321 is well known in the art, and thusdetailed descriptions thereof are not provided here. The reference imageis an image frame of which a shaking degree is the least among theplurality of image frames taken for the certain period of time. Theoptical flow calculator 1211 may receive the reference image from thereference image setting unit shown in FIG. 2. The method of setting thereference image is described above with reference to FIGS. 2 through 5.

The representative direction extractor 1221 inputs the optical flow 1321calculated by the optical flow calculator 1211. The representativedirection extractor 1221 extracts a representative shaking direction ofthe currently input image frame from the optical flow 1321. The shakingdirection of the image may be set in eight (8) directions, for example,east direction, west direction, south direction, north direction,south-east direction, north-east direction, south-west direction, andnorth-west direction. The representative direction extractor 1221determines which one of the eight directions is the representativedirection of the optical flow 1321 and sets the direction as therepresentative direction of the currently input image frame. The shakingdirection of the image may be divided in more detail, for example, 12directions, 24 directions, or 36 directions.

The representative magnitude extractor 1231 inputs the optical flow 1321calculated by the optical flow calculator 1211. The representativemagnitude extractor 1231 extracts a representative shaking magnitude ofthe currently input image frame from the optical flow 1321. Therepresentative shaking magnitude of the image frame may be obtained byconverting magnitudes of the optical flow having the representativeshaking direction into a histogram, and averaging vectors included in arange having the largest number of bins in the histogram.

The image moving unit 1241 moves the currently input image frame as muchas the representative magnitude extracted by the representativemagnitude extractor 1231 in an opposite direction to the representativedirection extracted by the representative direction extractor 1221. Thatis, the image moving unit 1241 moves the image frame as much as themagnitudes of Table 1 below in the directions shown in Table 1. In Table1, minus (−) denotes the opposite direction, and the representativedirections are the directions shown in FIG. 14.

TABLE 1 Representative coordinates of moving current image framedirection (X-axis, Y-axis) 1 −representative magnitude, 0 2−(representative magnitude/{square root over (2 )}), −(representativemagnitude/{square root over (2)}) 3 0, −representative magnitude 4(representative magnitude/{square root over (2 )}), −(representativemagnitude/{square root over (2)}) 5 representative magnitude, 0 6(representative magnitude/{square root over (2)}), (representativemagnitude/{square root over (2)}) 7 0, representative magnitude 8−(representative magnitude/{square root over (2)}), (representativemagnitude/{square root over (2)})

Referring to Table 1, the image moving unit 1241 moves the current imageframe on the X-axis as much as the representative magnitude in theopposite direction to the representative direction, when therepresentative direction is an X-axis (1, 5). In addition, the imagemoving unit 1241 moves the current image frame on the Y-axis as much asthe representative magnitude in the opposite direction to therepresentative direction when the representative direction is a Y-axis(3, 7). However, when the representative direction is a diagonaldirection (2, 4, 6, 8), the image moving unit 1241 moves the currentimage frame in a diagonal line as much as (representativemagnitude/√{square root over (2)}) in the opposite direction by usingtrigonometric functions. After that, four sides of the moved currentimage frame are trimmed in consideration of the representative directionand the representative magnitude. Therefore, the image may be stabilizedas shown in FIG. 15. The image moving unit 1241 outputs a signal P4representing the stabilized image.

FIGS. 16A and 16B are graphs showing shaken degrees of image frames.Specifically, FIG. 16A shows shaken degrees of image frames beforestabilization, and FIG. 16B shows shaken degrees of the image framesafter stabilization.

Referring to FIG. 16A, large deviation is shown between locations ofpixels in the image frames. That is, FIG. 16A shows a state where animage is severely shaken and is unstable.

Referring to FIG. 16B, small deviation is shown between locations of thepixels in the image frames. That is, FIG. 16B shows a state where theimage is stabilized.

As described above, the optical flow of the image input to the imageadjusting unit 131 is calculated to extract the representative directionand the representative magnitude of the image. Then, if the image isshaken, the image is moved as much as the representative magnitude inthe opposite direction to the representative direction. Thus, theshaking may be corrected and the image may be stabilized.

FIG. 17 is a flowchart illustrating a method of adjusting the image bythe image adjusting unit 131 shown in FIG. 12, according to anembodiment. Referring to FIG. 17, the method of adjusting the imageincludes operations S1711 through S1731.

In operation S1711, the image adjusting unit 131 compares the currentimage frame input from outside with the preset reference image tocalculate the optical flow (1321 of FIG. 13). The reference image isinput from outside to the image adjusting unit 131.

In operation S1721, the image adjusting unit 131 extracts therepresentative direction and the representative magnitude of the shakingof the currently input image from the optical flow 1321.

In operation S1731, the image adjusting unit 131 moves the image framethat is currently input as much as the representative magnitude in theopposite direction to the representative direction. In more detail, ifthe representative direction is an X-axis direction, the image adjustingunit 131 moves the current image frame as much as the representativemagnitude in the opposite direction to the representative direction onthe X-axis. If the representative direction is a Y-axis direction, theimage adjusting unit 131 moves the current image frame as much as therepresentative magnitude in the opposite direction to the representativedirection on the Y-axis. However, when the representative direction is adiagonal direction, the image adjusting unit 131 moves the current imageframe as much as (representative magnitude/√{square root over (2)}) inthe opposite direction to the representative direction on the diagonalline. After that, the four sides of the moved current image frame aretrimmed in consideration of the representative direction and therepresentative magnitude. Therefore, the shaking is corrected, and thestabilized image may be obtained as shown in FIG. 15.

According to the exemplary embodiments, the optimal characterizing pointchecking region is set by using the image frames extracted for a certainperiod of time, and thus a time to check the characterizing points ofthe currently input image frame may be reduced greatly.

In addition, since the shaking of the currently input image frame iscorrected by using the optimal characterizing point checking region, theimage correction time may be reduced and the image may be optimallystabilized.

While the inventive concept has been particularly shown and describedwith reference to exemplary embodiments thereof, it will be understoodby those of ordinary skill in the art that various changes in form anddetails may be made therein without departing from the spirit and scopeof the inventive concept as defined by the following claims.

What is claimed is:
 1. A characterizing point checking region settingunit comprising: a sample frame extract unit which extracts a pluralityof image frames, obtained for a certain period of time, from image dataobtained by photographing an object; and a frame analyzing unit whichdetects a plurality of characterizing points in the extracted pluralityof image frames, and sets a characterizing point checking region, whichis used to check the detected characterizing points, in a currentlyinput image frame, wherein the frame analyzing unit comprises: acharacterizing point detector which receives the plurality of imageframes from the sample frame extract unit, and detects the plurality ofcharacterizing points in the plurality of image frames; a characterizingpoint classification unit which classifies the plurality ofcharacterizing points into a plurality of clusters for each of the imageframes; a center point detector which detects a centroid point of thecharacterizing points in the plurality of image frames; and a checkregion determination unit which sets the characterizing point checkingregion including a major cluster among the plurality of clusters basedon the centroid point.
 2. The characterizing point checking regionsetting unit of claim 1, wherein a cluster of the plurality of clustersis classified as the major cluster if the cluster comprises 50% or moreof the characterizing points, and as a minor cluster if the clustercomprises less than 50% of the characterizing points.
 3. Thecharacterizing point checking region setting unit of claim 2, whereinthe center point detector calculates an average of a centroid point in amajor cluster of each of the plurality of image frames and of a centroidpoint in a minor cluster of the each of the plurality of image frames,sums the average values, and divides the summed average values by anumber of the plurality of image frames to detect the centroid point ofthe characterizing points in the plurality of image frames.
 4. Thecharacterizing point checking region setting unit of claim 1, whereinthe check region determination unit defines the characterizing pointchecking region as a square or a circle.
 5. The characterizing pointchecking region setting unit of claim 1, further comprising a checkingregion adjusting unit which determines whether the characterizing pointchecking region includes a standard level of characterizing points in animage frame of the image frames extracted for the certain period of timeor greater.
 6. The characterizing point checking region setting unit ofclaim 5, wherein the standard level is 80% of the characterizing points.7. A characterizing point checking region setting method comprising:receiving a plurality of image frames captured for a certain period oftime; and detecting a plurality of characterizing points in theplurality of image frames, and setting a characterizing point checkingregion for checking the detected characterizing points, in a currentlyinput image frame, wherein the setting the characterizing point checkingregion comprises: detecting a plurality of characterizing points in eachof the plurality of image frames; classifying the plurality ofcharacterizing points into a plurality of clusters for each of the imageframes; detecting a representative centroid point representing theplurality of image frames; and setting the characterizing point checkingregion including a major cluster among the plurality of clusters, basedon the representative centroid point.
 8. The characterizing pointchecking region setting method of claim 7, wherein a cluster of theplurality of clusters is classified as the major cluster if the clustercomprises 50% or more of the characterizing points, and as a minorcluster if the cluster comprises less than 50% of the characterizingpoints.
 9. The characterizing point checking region setting method ofclaim 8, wherein the representative centroid point is detected bydetecting centroid points of the major clusters and the minor clusters,calculating averages values of the centroid points in the clusters, anddividing the average values by the number of the plurality of imageframes to detect the representative centroid point.
 10. Thecharacterizing point checking region setting method of claim 7, furthercomprising checking whether the characterizing point checking regionincludes a standard level of characterizing points in an image frame ofthe image frames extracted for the certain period of time or greater.11. An image stabilizing apparatus comprising: the characterizing pointchecking region setting unit of claim 4; an image adjusting unit whichsets the characterizing point checking region in an image frame that iscurrently input, compares the currently input image frame with areference image that is preset, and adjusts the currently input imageframe as much as a shaking amount when it is determined that the currentimage frame is shaken; and reference image setting unit which extractsan image frame that is the least shaken among the plurality of imageframes taken for a certain period of time, and sets the extracted imageframe as the reference image, wherein the reference image setting unitextracts the least shaken image frame by determining distances between aplurality of characterizing points and a center point in each of theimage frames.
 12. The image stabilizing apparatus of claim 11, whereinthe reference image setting unit comprises: a center point detectorwhich receives the plurality of image frames and detects the centerpoint in each of the image frames; a characterizing point detector whichdetects the plurality of characterizing points in each of the imageframes; a frame average calculator which calculates a plurality of frameaverages each of which is an average of the distances between the centerpoint and the plurality of characterizing points in each of the imageframes; a frame comparison value calculator which calculates a pluralityof frame comparison values each of which is obtained by summing upabsolute values each of which is obtained by subtracting, from the frameaverage of each of the frame, a frame average of another image frame;and a reference frame selector which selects an image frame having thesmallest frame average value as the reference image.